Yearly Traffic Safety Analysis

295 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In Fayette County, total traffic crashes decreased by 15.0% from 347 in 2018 to 295 in 2019. Despite this overall reduction, total fatalities increased from 4 to 5, and crashes involving a suspected DUI driver more than doubled, rising from 4 incidents in 2018 to 9 in 2019.

295

▼ -15.0%was 347

Total Crash Events

5

▲ 25.0%was 4

Persons Killed

89

▼ -2.2%was 91

Persons Injured

4

Fatal Crash Events

Note: "Persons Killed" (5) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend in Fayette County shows a decrease in traffic incidents, with total crashes falling from 347 to 295 year-over-year. The number of people injured also saw a slight decline from 91 to 89. However, the number of fatalities rose from 4 in 2018 to 5 in 2019.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

5

Motorists Killed

Prior: 4↑25.0%

2

Pedestrians Injured

Prior: 1↑100.0%

1

Cyclists Injured

Prior: 0→%

86

Motorists Injured

Prior: 90↓-4.4%

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes remained consistent year-over-year. Monday was the peak day for crashes in both 2019 (53 crashes) and 2018 (64 crashes). Similarly, the 5 p.m. hour was the most frequent time for incidents in both periods, accounting for 25 crashes in 2019 and 32 in 2018.

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While the number of fatal crashes was unchanged at 4 in both periods, the fatal crash rate increased from 1.15% to 1.36% due to the lower total number of crashes in 2019. The proportion of crashes resulting in any type of injury was stable at 19.7% in 2019 compared to 19.0% in 2018. Crashes involving serious injuries decreased slightly from 10 to 7.

Severity is per crash event (most severe injury). 4 fatal crash events resulted in 5 persons killed.

Outcome by Severity (Crash Events)

Fatal4fatal crashes1.4%
→0.0%prior 4
Serious Injury7serious injury crashes2.4%
↓-30.0%prior 10
Minor Injury26minor injury crashes8.8%
↓-21.2%prior 33
Possible Injury25possible injury crashes8.5%
↑8.7%prior 23
No Injury233no injury crashes79%
↓-15.9%prior 277

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions with animals remained the leading contributing factor in both years, though the count of these incidents fell by 34% from 146 in 2018 to 96 in 2019. "Lost Control" was the second-most cited factor in both periods, with a stable count of 32 incidents in 2019 versus 30 in 2018. Notably, crashes where a driver ran a stop sign increased from 3 in 2018 to 14 in 2019.

Officer-Reported Primary Contributing Cause

Animal96 (32.5%)↓-34.2%prior 146
Lost Control32 (10.8%)↑6.7%prior 30
Driving too fast for conditions26 (8.8%)↑23.8%prior 21
Other (explain in narrative): Other16 (5.4%)↓-15.8%prior 19
Ran off road - left15 (5.1%)↑15.4%prior 13
Ran Stop Sign14 (4.7%)
FTYROW: From stop sign14 (4.7%)↑75.0%prior 8
Ran off road - straight11 (3.7%)↓-50.0%prior 22
Followed too close8 (2.7%)↓-27.3%prior 11
Made improper turn6 (2%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The proportion of crashes occurring in daylight was higher in 2019 (49.1%) than in 2018 (44.1%). A significant shift was observed in road surface conditions, with crashes on icy or frosty roads increasing from 17 in 2018 to 46 in 2019. Consequently, the share of crashes on icy roads rose from 4.9% to 15.6% of all incidents.

Weather

Clear130 (61.3%)
↓-13.3%prior 150
Cloudy39 (18.4%)
↑5.4%prior 37
Snow12 (5.7%)
→0.0%prior 12
Freezing rain/drizzle12 (5.7%)
↓-7.7%prior 13
Blowing Snow8 (3.8%)
Rain7 (3.3%)
↓-46.2%prior 13
Fog, smoke, smog3 (1.4%)
Sleet, hail1 (0.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Weather condition at time of crash

Lighting

Daylight145 (68.4%)
↓-5.2%prior 153
Dark - roadway not lighted46 (21.7%)
↑4.5%prior 44
Dark - roadway lighted11 (5.2%)
↓-31.3%prior 16
Dusk7 (3.3%)
↓-36.4%prior 11
Dawn3 (1.4%)
↓-40.0%prior 5

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Lighting condition field

Road Surface

Dry112 (52.8%)
↓-25.8%prior 151
Ice/frost46 (21.7%)
↑170.6%prior 17
Snow22 (10.4%)
↑15.8%prior 19
Wet13 (6.1%)
↓-40.9%prior 22
Gravel13 (6.1%)
↑8.3%prior 12
Slush5 (2.4%)
↓-28.6%prior 7
Other (explain in narrative)1 (0.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Road surface condition field

Vehicles & Demographics

Chevrolet and Ford were the top two vehicle makes involved in crashes in both years, though both saw their counts decrease in 2019. A notable demographic shift occurred among persons involved in crashes; involvement for the 16-20 age group decreased from 96 to 79 people, while the 26-34 age group saw an increase from 64 to 89 people.

Top Vehicle Makes (405 vehicles)

1
FORD93 (23%)
↓-12.3%prior 106
2
CHEV71 (17.5%)
↓-25.3%prior 95
3
CHEVROLET25 (6.2%)
↓-3.8%prior 26
4
GMC21 (5.2%)
↑90.9%prior 11
5
DODG21 (5.2%)
↓-4.5%prior 22
6
CHRY17 (4.2%)
↑30.8%prior 13
7
JEEP14 (3.5%)
↑27.3%prior 11
8
DODGE11 (2.7%)
↑120.0%prior 5
9
BUIC10 (2.5%)
→0.0%prior 10
10
PONTIAC9 (2.2%)
↑80.0%prior 5

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Vehicle unit records

34 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (385 persons with recorded sex)

Male215 (55.8%)
↑11.4%prior 193
Female170 (44.2%)
↑23.2%prior 138

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Person-level records linked to crash events

Data Sources & Methodology

Primary Data Source

All crash data in this report is sourced from Iowa Crash Data, accessed programmatically via the ArcGIS Open Data API (SODA). This dataset contains official police-reported motor vehicle traffic crash records maintained by the reporting jurisdiction's law enforcement agency. Records are published to the open data portal by the municipality and are subject to the portal's terms of use.

Data Retrieval

  • Access method: ArcGIS Open Data API (SoQL queries)
  • Data format: Structured JSON via REST API
  • Record types queried: Crash events, person records, and vehicle unit records
  • Date filter applied: 2019-01-01 through 2019-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2019-01-01 through 2019-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 295
  • Total persons involved: 598
  • Total vehicles involved: 405

Analytical Methodology

  • Severity classification: Uses the KABCO injury scale (K=Fatal, A=Incapacitating injury, B=Non-incapacitating injury, C=Possible injury, O=No injury/property damage only), the standard classification in U.S. Model Minimum Uniform Crash Criteria (MMUCC). Severity is assigned per crash event based on the most severe injury in that crash. A single fatal crash (K) may involve multiple fatalities; therefore the "Persons Killed" count in the headline KPIs may differ from the "Fatal" crash count in the severity breakdown.
  • Contributing factors: Reflect the officer-determined primary contributory cause recorded at the time of the crash report. These are preliminary determinations and may not reflect final investigation findings.
  • Hit-and-run classification: Based on the hit-and-run indicator field in the official crash report, as determined by the responding officer at the scene.
  • Temporal analysis: Day-of-week and hour-of-day distributions are computed from the crash date/time timestamp in each record.
  • Demographics: Age and sex distributions are drawn from person-level records linked to each crash event. A single crash may involve multiple persons.
  • Vehicle data: Make information is drawn from vehicle unit records linked to each crash event.
  • AI commentary: Narrative sections are generated by Google Gemini (large language model) based on the structured data. Commentary is descriptive, not predictive, and should not be interpreted as expert opinion.

Limitations & Disclaimers

  • Only crashes reported to and documented by law enforcement are included. Minor incidents, unreported crashes, and near-misses are not captured in this dataset.
  • Data reflects conditions at the time of the initial police report and may be subject to subsequent corrections, reclassifications, or supplements by the reporting agency.
  • Open data portal records may experience a publication lag - recently occurring crashes may not yet appear in the dataset at the time of report generation.
  • AI-generated commentary is produced by a large language model and is intended to highlight patterns in the data. It does not constitute legal, medical, or professional analysis.
  • Percentages are calculated from reported data and are subject to rounding.

Non-Affiliation Disclosure

This report is produced independently by ThatCarHitMe.com (Injuria.ai). It is not affiliated with, endorsed by, or produced in partnership with any law enforcement agency, municipal government, state department of transportation, or the National Highway Traffic Safety Administration (NHTSA). Data is sourced from publicly available government open data portals.

Data License

The underlying crash data is provided under the municipality's Open Data Terms of Use and is made available to the public for unrestricted use. This analysis and report is © 2026 Injuria.ai and may be cited with attribution using the suggested citation below.

Corrections & Feedback

If you believe any data in this report is inaccurate or have questions about our methodology, please contact: data@injuria.ai. We are committed to accuracy and will issue corrections promptly.

Suggested Citation

ThatCarHitMe.com (Injuria.ai). "iowa, IA Crash Intelligence Report: 2019." Published September 9, 2026. Reporting period: 2019-01-01 to 2019-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2019-annual-report

About the Publisher

ThatCarHitMe.com is a crash data intelligence platform developed by Injuria.ai, a legal technology company specializing in traffic safety analytics. We aggregate and analyze publicly available government crash data to produce structured intelligence reports for communities, researchers, journalists, and legal professionals. Our reports combine programmatic data retrieval from official open data portals with AI-assisted narrative analysis.

Questions about this report's data or methodology: data@injuria.ai

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